/resources/basics/image-vs-video-processing/index.md · 63 lines
---
title: "Image vs. video processing"
description: "When a single frame is enough, and when you need motion, sequence and time."
url: https://lidvizion.ai/resources/basics/image-vs-video-processing/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---
[Basics](https://lidvizion.ai/resources/) Vision
# Image vs. video processing
When a single frame is enough, and when you need motion, sequence and time.
## Images answer “what is here?”
A single image is enough when the question is about a state: is the form complete, is the item damaged, which objects are present. Images are cheaper to store, label and process, and easier to review.
## Video answers “what happened?”
Video adds time. It lets a system count repetitions, follow an object across frames, measure speed or range of motion, and notice events that only make sense in sequence.
- Tracking links detections across frames so each object keeps an identity.
- Sampling decides how many frames per second are worth processing.
- Temporal logic turns per-frame results into events, such as one completed repetition.
| | Image | Video |
| --- | --- | --- |
| Best for | States and inspections | Motion, counts and events |
| Cost | Lower | Higher: more frames, more storage |
| Review | One frame per decision | Clips and timelines |
| Typical output | Labels, boxes, fields | Tracks, counts, timings |
## Choosing between them
Start with images if the decision can be made from one moment. Move to video when the answer depends on movement or order, and budget for the extra compute, storage and review effort.
Put it to work
## Build the missing piece
Custom tools and applications for the gaps that matter.
[How we do it](https://lidvizion.ai/services/custom-software/)
Keep reading
## More basics.
[All basics](https://lidvizion.ai/resources/)
- [What is computer vision?](https://lidvizion.ai/resources/basics/what-is-computer-vision/): Software that turns images and video into structured information a system can act on.
- [Embeddings and feature vectors](https://lidvizion.ai/resources/basics/embeddings-feature-vectors/): How models turn text, images and records into numbers that can be compared.
- [Fine-tuning vs. pre-trained models](https://lidvizion.ai/resources/basics/model-fine-tuning-vs-pre-trained-models/): Why most teams should start with a capable pre-trained model, and when fine-tuning earns its cost.
Bring the messy version
## One workflow. A better way to run it.
Show us the tools, the handoffs, and the part that keeps slowing your team down.
[Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)